Irish and US researchers improve biometric age estimation with facial embeddings
A team of scientists from University College Dublin, Intel Ireland, and The University of Texas at San Antonio has improved…
training
What information is stored in face biometric templates? EAB explores
Deeply-learned face representations enable the success of current facial recognition systems today. Despite the ability of facial recognition systems representations…
Facebook self-supervised computer vision model promises object recognition breakthrough
Facebook has announced the development of a new computer vision model, named SEER (SElf-supERvised). SEER has been pre-trained on a…
ACM FAccT increases participant diversity to investigate AI social justice impacts
The Association for Computing Machinery (ACM) will hold a virtual conference from March 3rd to 10th on the topics of…
Datasets behind face biometrics reveal fields’ ethical backslide: research paper
Face biometrics researchers have largely abandoned a set of data collection practices in order to fuel the voracious appetite of…
Online tool exposes whether face biometrics have been trained with your photos
Exposing.AI is an online tool that can help users to track whether their pictures have been used to train AI…
The internet’s small-mindedness makes AI brutish as well
If bias cannot be wrung from the process of training biometrics and other AI algorithms, maybe unsupervised algorithms can learn…
Onfido’s use of biometric data for bias research meets legal requirements — ICO report
The UK Information Commissioner’s Office (ICO) has released the report of regulatory sandbox test carried out with Onfido, which concluded…
Computer vision modelers take too much for granted. Data sets hold bias surprises
A pair of U.S. researchers say unsupervised computer vision models used in biometrics and other applications can learn nasty social…
Method for facial recognition bias reduction with adversarial network shows promise
A prominent trio of biometrics researchers have proposed a way to remove the difference, or bias, from facial recognition performance…
